It conducts the first stage analysis of TSSEM by pooling
correlation/covariance matrices. tssem1FEM() and
tssem1REM() use fixed- and random-effects models,
respectively. tssem1() is a wrapper of these functions.
tssem1(Cov, n, method=c("REM","FEM"), cor.analysis = TRUE, cluster=NULL,
RE.type=c("Diag", "Symm", "Zero", "User"), RE.startvalues=0.1,
RE.lbound=1e-10, RE.constraints=NULL, I2="I2q",
acov=c("weighted", "individual", "unweighted"),
model.name=NULL, suppressWarnings=TRUE, silent=TRUE, run=TRUE, ...)
tssem1FEM(Cov, n, cor.analysis=TRUE, model.name=NULL,
cluster=NULL, suppressWarnings=TRUE, silent=TRUE, run=TRUE, ...)
tssem1REM(Cov, n, cor.analysis=TRUE, RE.type=c("Diag", "Symm", "Zero","User"),
RE.startvalues=0.1, RE.lbound=1e-10, RE.constraints=NULL,
I2="I2q", acov=c("weighted", "individual", "unweighted"),
model.name=NULL, suppressWarnings=TRUE,
silent=TRUE, run=TRUE, ...)A list of correlation/covariance matrices
A vector of sample sizes
Either "REM" (default if missing) or "FEM".
If it is "REM",a random-effects meta-analysis will be applied. If it
is "FEM", a fixed-effects meta-analysis will be applied.
Logical. The output is either a pooled correlation or a covariance matrix.
A vector of characters or numbers indicating the
clusters. Analyses will be conducted for each cluster. It will be
ignored when method="REM".
Either "Diag", "Symm",
"Zero" or "User". If it is
"Diag", a diagonal matrix is used for the random effects
meaning that the random effects are independent. If it is "Symm" (default if missing), a
symmetric matrix is used for the random effects on the covariances
among the correlation (or covariance) vectors. If it is
"Zero", there is no random effects which is similar to the
conventional Generalized Least Squares (GLS) approach to
fixed-effects analysis.
"User", the user has to specify the variance component via the
RE.constraints argument. This argument will be ignored when method="FEM".
Starting values on the
diagonals of the variance component of the random effects. It will be ignored when method="FEM".
Lower bounds on the diagonals of the variance
component of the random effects. It will be ignored when
method="FEM".
A \(p*\) x \(p*\) matrix
specifying the variance components of the random effects, where
\(p*\) is the number of effect sizes. If the input
is not a matrix, it is converted into a matrix by
as.matrix(). The default is that all
covariance/variance components are free. The format of this matrix
follows as.mxMatrix. Elements of the variance
components can be constrained equally by using the same labels. If a zero matrix is
specified, it becomes a fixed-effects meta-analysis.
Possible options are "I2q", "I2hm" and
"I2am". They represent the I2 calculated by using a
typical within-study sampling variance from the Q statistic, the
harmonic mean and the arithmetic mean of the within-study sampling
variances (Xiong, Miller, & Morris, 2010). More than one options are possible. If intervals.type="LB", 95% confidence intervals on the heterogeneity indices will be constructed.
If it is individual, the sampling variance-covariance
matrices are calculated based on individual correlation/covariance
matrix. If it is either unweighted or weighted (the default), the average
correlation/covariance matrix is calculated based on the unweighted
or weighted mean with the sample sizes. The average
correlation/covariance matrix is used to calculate the sampling
variance-covariance matrices. This argument is ignored with the
method="FEM" argument.
A string for the model name in mxModel.
Logical. If TRUE, warnings are
suppressed. It is passed to mxRun.
Logical. An argument to be passed to mxRun
Logical. If FALSE, only return the mx model without running the analysis.
Further arguments to be passed to mxRun
Either an object of class tssem1FEM for fixed-effects TSSEM,
an object of class tssem1FEM.cluster for fixed-effects TSSEM
with cluster argument, or an object of class tssem1REM
for random-effects TSSEM.
Cheung, M. W.-L. (2014). Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46, 29-40.
Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454.
Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40-64.
Cheung, M. W.-L., & Chan, W. (2009). A two-stage approach to synthesizing covariance matrices in meta-analytic structural equation modeling. Structural Equation Modeling, 16, 28-53.